20
Background (2) YouTube – a representative Popular Market share of around 43% More than six billion videos viewed in January 2009 Consumed as much bandwidth as the entire Internet in rd visits among all Internet sites (after Google and Yahoo) Fast growing 20% growth rate per month 15 hours of new videos are uploaded every minute

21
Motivation (1) The YouTube Crisis – all other sites’ challenge Severely hindered by client/server architecture Bandwidth costs Consumed as much bandwidth as the entire Internet in 2000 $1 million a day for server bandwidth! Sold to Google for $1.65 billion in Nov Performance and scalability “Slow” among the surveyed sites by Alexa.com

30
Data-Driven Mesh Core operations Every node periodically exchanges data availability information with a set of partners Then retrieves unavailable data from one or more partners, or supplies available data to partners Easy to implement no need to construct and maintain a complex global structure Efficient data forwarding is dynamically determined according to data availability Robust and resilient adaptive and quick switching among multi-suppliers

31
Challenges and Opportunities (1) Challenges – Drastically different statistics 1.5 year measurement of 5 million videos Short video clips – stability 99.6% are less than 700 seconds “I don’t want to wait for 30 seconds for a two-minute video!”  Searching for sources High churn rate: join/leave system Huge number of videos – scalability Highly skewed Inefficient for unpopular videos Very few users watch the same one

53
Limitations of Tit-for-Tat Tit-for-Tat is synchronous trading Alice and Bob can trade if and only if they simultaneously have data for each other in a short time period Tit-for-Tat == Barter ( 物物交换 ) in primitive economy Barter is highly inefficient fails if lack of “double coincidence of wants” failure example: Tit-for-tat does not provide incentive for seeding 53

61
P2P Trading in Social Networks Networked Asynchronous Bilateral Trading (NABT) Social network: peers belong to an underlying social network Pair-wise credit: friends maintain pair-wise credits Asynchronous trading: peers can use their credits anytime they want Credit limit: each peer sets a credit limit for each of its friends Networked trading: peer trades with a remote peer by transferring credits through a chain of friends links. 61

66
NABT Efficiency Single trade can be exercised if and only if a credit transfer can be arranged subject to social network connectivity obey credit limit on each social link Multiple trades coupled through the underlying social network later trades work with credit balance resulted from earlier trades concurrent trades compete for credit transfer 66

69
Balanced Demand For each user k, total service he provides (regardless of receivers) equals total service received (regardless of providers) Theorem 1: Any balanced demand can be executed as long as users involved in the demand sets are connected. NABT is as efficient as global currency networked Tit-for-Tat: peers play tit-for-tat with whole network instead of another peer 69

70
Unbalanced Demand For at least one user, service contribution does not equal to service consumption. net-service contribution: service sources: service sinks: aggregate net-service imbalance 70

72
Efficiency with Unbalanced Demand Theorem: An unbalanced demand is executable iff the min-cut between the source s+ and sink s- in extended social network is greater than or equal to the aggregate net-service imbalance. What matters: underlying social network topology credit limits on social links service imbalance between a user and whole network What does not matter: service imbalance between individual pairs of users 72

73
Dynamic Payment Routing Time is slotted Demands are now sequential H(1), H(2),… Suppose we succeed at executing H(1),…, H(k-1). Theorem: To successfully execute H(k), we do not have to worry about how we executed H(1),…,H(k-1). 73

76
Simulation Study Trading with global currency (GCT): Global currency and a centralized bank Each peer has Bi initial credits and each file costs one credit If peer i downloads a file from peer j, peer i pays 1 credit to peer j Synchronous Trading (ST): Two peers can trade if and only if they can supply files to each other simultaneously If peer i downloads a file from peer j, peer j will download a file from peer i. Two-hop NABT: Peers are connected in an underlying social network A requesting peer requests files from its friends (one-hop friends) and the friends of its friends (two-hop friends) If peer i downloads a file from peer j within two hops, peer i passes 1 credit to peer j 76

94
SocialMF [RecSys2010] Social Influence  behavior of a user u is affected by his direct neighbors. Latent factor of a user depend on his neighbors. is the normalized trust value. Prediction Model: Objective: 94

95
Proposed Improvements for Current Social Recommender Social networks include multiple circles A more refined social trust information—richer information Incorporate circle information in Social Recommender Use trust circles specific to an item category when predict rating in this category e.g. Trust Circle of “Music”, Trust Circle of “Cars”, etc 95

96
Proposed Improvements for Current Social Recommender 96 Existing circles (Google+, Facebook) not corresponding to an item category

97
Proposed Improvements for Current Social Recommender In existing multi-category rating datasets, no circle information User trusts different subsets of friends in different domains (Cars, Music…) User trusts different friends differently, related to friend’s expertise value Should use trust circle specific to item category 97

99
Trust Circle Inference User v is in inferred circle c of u iff u trust v in original social network and both of them have rating in category c 99 Original Social Network Inferred circle for category C1 Inferred circle for category C 2 Inferred circle for category C 3

116
Training with ratings from all categories 116 CircleCon3 of training with per-category rating

117
Training with ratings from all categories 117

118
Training with ratings from all categories 118

119
Summary Propose a novel Circle-based Social Recommendation framework Split original social network to different circles, one circle corresponding to one item category User trusts different subsets of friends in different domains(Cars, Music…) User trusts different friends differently, based on friend’s expertise Outperforms the state-of-the-art social collaborative filtering algorithms Show the promising future of circle-construction techniques in Social Recommender 119

120
小结 120 Social networking has been changing the way which people communicate!